GPT-6 Astra vs Sol vs Luna: Reasoning Cost Guide 2026

GPT-6 Astra is not automatically the best default for every workload, and the cheapest GPT-6 model can become surprisingly capable when it receives more reasoning time.

Last reviewed: October 12, 2026

For most production workloads, GPT-6.1 Sol at medium or high reasoning is the best place to start. GPT-6 Luna is the economical choice for high-volume, easy-to-verify work, while GPT-6 Astra should be reserved for tasks where its final capability advantage changes the outcome. LLMFly AI makes this comparison practical by providing supported GPT, Claude, Gemini, and Grok models through one multi-model AI API platform, with current GPT routes listed below provider reference prices.

Quick answer: Which GPT-6 model and reasoning level should developers use?

A useful default routing policy is:

GPT-6 Luna: classification, extraction, routing, cleanup, simple summaries, and bounded subagent work.
GPT-6.1 Sol medium or high: everyday production coding, debugging, research synthesis, computer use, and multi-step agents.
GPT-6 Astra: the hardest coding, scientific, architectural, or high-cost-of-failure tasks where evaluations show a measurable advantage.

Start with the lowest reasoning level that reliably passes your own evaluation set. Increase reasoning effort before moving up a model tier, then compare both options on cost per accepted result—not only price per million tokens.

Model	Official input / 1M	Official cached input / 1M	Official output / 1M	Best starting use
GPT-6 Astra	$10.00	$1.00	$50.00	Maximum capability
GPT-6.1 Sol	$2.00	$0.10	$10.00	Serious coding and agents
GPT-6 Luna	$0.10	$0.01	$0.50	High-volume bounded work

These are OpenAI’s standard short-context prices as reviewed on October 12, 2026. Cache writes, tools, processing tiers, regional processing, and long-context requests can add separate charges.

Which reasoning levels do GPT-6 Astra, GPT-6.1 Sol, and GPT-6 Luna support?

OpenAI exposes reasoning effort as a controllable API parameter. The supported values are not identical across the three models.

Model	Supported reasoning efforts	Default
GPT-6 Astra	low, medium, high, xhigh, max	Check the current model documentation
GPT-6.1 Sol	low, medium, high, xhigh, max	medium
GPT-6 Luna	none, low, medium, high, xhigh, max	medium

Astra does not support none. GPT-6.1 Sol supports neither none nor minimal. Luna is the most flexible option for requests that need little or no reasoning.

OpenAI recommends the Responses API for reasoning models. For GPT-6 Astra and GPT-6.1 Sol, tool calling is supported through the Responses API rather than Chat Completions. Luna can use function calling through Chat Completions only when reasoning_effort is set to none.

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-6.1-sol",
    reasoning={"effort": "high"},
    input="Review this patch, identify correctness risks, and propose tests."
)

print(response.output_text)
print(response.usage)

Reasoning tokens are billed as output tokens. Set max_output_tokens high enough to leave room for reasoning, but treat it as a real spending control. A request can consume reasoning tokens and still end as incomplete before producing visible output.

How large is the price gap between Astra, Sol, and Luna?

At standard short-context rates, Astra costs five times as much as GPT-6.1 Sol for both input and output. It costs 100 times as much as Luna at the headline input and output rates.

Those ratios are useful, but they do not predict the final cost of a completed agent task. Higher reasoning effort can generate more billable reasoning tokens. A weaker configuration may also require more retries, more tool calls, or human correction.

The practical equation is:

cost per accepted result =
  (input + cache writes + cache reads + output + tools + retries)
  / accepted results

This is why a $0.10-per-million input model is not necessarily cheap for a task it repeatedly fails, and a $10-per-million model is not necessarily expensive when one successful run prevents costly rework.

What do independent benchmarks say about GPT-6.1 Sol versus Astra?

Artificial Analysis currently scores GPT-6.1 Sol Max at 52 on its Intelligence Index. Its evaluation reports an average cost of approximately $0.72 per Intelligence Index task for that configuration. Artificial Analysis places Astra Max one point higher in the aggregate comparison, while estimating a materially higher cost per task.

These figures are third-party benchmark estimates, not OpenAI billing promises. They depend on Artificial Analysis’ prompts, output lengths, benchmark weights, and execution settings. Use them to identify candidates—not to forecast your production bill.

OpenAI’s own release material reaches a similar high-level conclusion. The company positions GPT-6.1 Sol as near-Astra capability for one-fifth of Astra’s standard input and output prices. On OpenAI’s published evaluations, the remaining gap varies by workload: Sol can match Astra on some coding tasks, approach it on computer use and professional work, and remain behind on the most difficult scientific workflows.

Can GPT-6.1 Sol at high reasoning beat Astra at low reasoning?

Yes, on some benchmark mixes. Artificial Analysis reports configurations where Sol High scores above Astra Low while costing less per evaluated task.

That does not establish a universal ranking. It establishes a more useful principle: model tier and reasoning level must be tested together.

A team comparing only Astra versus Sol may miss the configuration that actually sits on its best price-performance frontier. The evaluation matrix should include at least:

Sol medium versus Sol high;
Sol high versus Astra low;
Luna high or max for tasks with simple acceptance criteria;
Astra high or max only for workloads where the premium might change the result.

When is GPT-6 Luna the right choice?

Luna is designed for focused, high-volume work. It is not intended to win every frontier reasoning benchmark. Its advantage is that it can process large numbers of inexpensive, verifiable tasks.

Good candidates include:

classification and tagging;
structured extraction;
content normalization and cleanup;
request routing;
short summaries;
schema-constrained transformations;
simple subagent tasks with deterministic validation.

Luna supports reasoning efforts from none through max, so developers can increase inference-time work without immediately moving to Sol. However, repeatedly running Luna Max on a task that still fails can be worse than sending the task to Sol once.

The correct rule is not “always use the cheapest model.” It is “use the cheapest configuration that passes.”

Is maximum reasoning effort worth the cost?

Usually not as a global default.

Independent evaluations of GPT-6.1 Sol show large gains when moving from low to medium reasoning, followed by smaller gains toward high, xhigh, and max. The exact curve varies by benchmark, but the pattern illustrates diminishing returns.

If medium effort passes a structured extraction test at the required accuracy, high effort buys unnecessary reasoning tokens. If high passes 98% of repository tasks, max needs to improve enough of the remaining 2% to justify its extra cost and latency.

Use max when:

the problem is genuinely difficult or ambiguous;
failure carries meaningful downstream cost;
the model must coordinate several tools or long dependencies;
your own evaluation shows a material improvement over high.

Do not use max simply because it is available.

What did Simon Willison’s pelican test demonstrate?

Simon Willison asked multiple models and reasoning levels to generate SVG pelicans riding bicycles. The test was deliberately playful, not a formal benchmark, but the comparison grid included two details that ordinary leaderboards often omit: token use and final cost.

His observation was useful: Astra’s higher token rate did not translate directly into the same multiple of completed-task cost because Astra could sometimes produce a better result with fewer tokens. The test also made the visible effect of reasoning effort easy to inspect.

The lesson is not that pelican SVGs predict production coding quality. The lesson is that developers should compare outputs, tokens, retries, and price together.

How should teams calculate cost per accepted result?

Suppose Sol High costs $0.30 per run and 80% of runs pass review:

$0.30 / 0.80 = $0.375 per accepted result

If Astra High costs $1.50 per run and passes 99%:

$1.50 / 0.99 = approximately $1.52 per accepted result

Sol remains cheaper in this simplified example. But the answer can change if a failed run triggers expensive tools, human review, a rollback, or an incorrect business decision.

A useful evaluation record should capture:

pass rate and acceptance criteria;
uncached and cached input tokens;
reasoning and visible output tokens;
tool calls and subagent calls;
retries and fallbacks;
latency;
human correction time;
total API cost.

What is a practical GPT-6 routing strategy?

Tier 1: Start with GPT-6 Luna

Use Luna for inexpensive, bounded work with clear validation. Start with none, low, or medium reasoning where possible. Increase effort only if it improves the pass rate enough to matter.

Tier 2: Escalate to GPT-6.1 Sol

Use Sol for production coding, debugging, research synthesis, agent planning, complex document work, and computer use. Medium is the official default; medium and high are the most sensible first configurations to benchmark.

Tier 3: Reserve GPT-6 Astra for measurable advantage

Use Astra for the hardest technical, scientific, architectural, and autonomous workflows. Astra should earn its place through a higher success rate, fewer retries, lower review cost, or better outcomes on tasks where failure is expensive.

How does the 272K long-context threshold affect GPT-6 pricing?

OpenAI applies long-context pricing when a request contains more than 272,000 input tokens. The higher rate applies to the entire request, not only the tokens above the threshold.

Model	Short-context input / output	Long-context input / output
GPT-6 Astra	$10 / $50	$20 / $75
GPT-6.1 Sol	$2 / $10	$4 / $15
GPT-6 Luna	$0.10 / $0.50	$0.20 / $0.75

For repository agents and long-running conversations, context design can save more money than changing reasoning from high to medium. Track repeated history, tool definitions, repository context, cache hits, and compaction before deciding the model itself is too expensive.

How much do GPT-6 Astra and GPT-6.1 Sol cost on LLMFly AI?

As of October 12, 2026, the LLMFly AI pricing page lists its ChatGPT/Codex model group at a 0.3× billing multiplier, or 70% below the provider reference prices displayed on that page.

Model	Provider reference input / output	Current LLMFly input / output
GPT-6 Astra	$10 / $50	$3 / $15
GPT-6.1 Sol	$2 / $10	$0.60 / $3

These rates are per million tokens for requests within the standard context tier. Cache writes, cache reads, tools, retries, and long-context pricing are separate. Because rates and availability can change, use the live Model Plaza as the billing source of truth before projecting monthly spend.

For a representative request containing 100,000 uncached input tokens and 10,000 output tokens:

Route	Basic token cost
Astra at provider reference rates	$1.50
Astra at current LLMFly listed rates	$0.45
Sol at provider reference rates	$0.30
Sol at current LLMFly listed rates	$0.09

The example excludes caching, tools, retries, and other charges. It demonstrates why two optimizations can compound: choose the least expensive model configuration that passes, then run it at a lower available token rate.

Why does a multi-model API matter for reasoning-cost optimization?

The price-performance frontier moves quickly. Today’s best configuration may be Sol High; another workload may favor Astra Low, a Claude model, or a Gemini release.

LLMFly AI provides one platform for supported GPT, Claude, Gemini, and Grok models. Developers can compare the current model catalog, check live pricing, use separate application keys, and test model alternatives without maintaining a separate billing and integration stack for every experiment. OpenAI-compatible access is available for compatible routes, but protocol compatibility does not mean that different models behave identically.

A simple evaluation table is often enough:

Candidate	Reasoning	Pass rate	Average cost	P95 latency
Luna	high	Measure	Measure	Measure
Luna	max	Measure	Measure	Measure
Sol	medium	Measure	Measure	Measure
Sol	high	Measure	Measure	Measure
Astra	low	Measure	Measure	Measure

Run the same representative workload, define acceptance before testing, and select the cheapest row that reliably passes.

Which GPT-6 model should you choose?

Choose GPT-6 Luna when volume matters more than frontier reasoning and the result is easy to validate.
Choose GPT-6.1 Sol when you need serious reasoning, coding, or agent capability while remaining cost-conscious.
Choose GPT-6 Astra when the marginal capability improvement is worth much more than the additional inference cost.

Within each model, begin with the lowest reasoning effort that meets the quality threshold. Escalate only when higher effort improves accepted outcomes.

Frequently asked questions

Is GPT-6.1 Sol better value than GPT-6 Astra?

For many coding, agent, and professional workloads, yes. OpenAI positions Sol as near-Astra capability at one-fifth of Astra’s standard input and output prices. Astra remains appropriate when its higher success rate on a specific difficult task justifies the premium.

Does higher reasoning effort change the token price?

The listed per-token rate stays tied to the model and processing tier, but higher reasoning can generate more billable reasoning tokens. Those tokens are counted as output tokens, so the final request cost can increase.

Should I use GPT-6 Luna Max or GPT-6.1 Sol Low?

Test both. Luna Max can be attractive for inexpensive, verifiable work, while Sol Low may be more reliable on complex tasks. Compare pass rate, token use, latency, retries, and total cost per accepted result.

Can I use GPT-6 reasoning models through Chat Completions?

Chat Completions remains available, but OpenAI recommends Responses for reasoning models. Tool calling for GPT-6 Astra and GPT-6.1 Sol requires Responses. Luna supports function calling through Chat Completions only with reasoning effort set to none.

Do reasoning tokens appear in the visible answer?

No. They are internal, but the API usage object reports their count under output-token details. They consume context and are billed as output tokens.

Bottom line

GPT-6 reasoning effort is now part of model selection. Luna provides inexpensive intelligence at scale. GPT-6.1 Sol sits close to the frontier at a much lower list price. Astra remains the premium option for workloads where the final capability margin materially changes outcomes.

The best configuration is not the one with the highest benchmark score. It is the least expensive configuration that reliably crosses your quality threshold. Measure cost per accepted result, include the full agent loop, and keep model switching easy enough to repeat the evaluation when prices or models change.

Sources

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GPT-6 Astra vs GPT-6.1 Sol vs GPT-6 Luna: Which Reasoning Level Is Actually Worth the Cost?